class_docstr: Handle end-to-end training and deployment of custom PyTorch code.
class_docstr_tok: ["Handle","end-to-end","training","and","deployment","of","custom","PyTorch","code","."]
docstr: Return hyperparameters used by your custom PyTorch code during model training.
docstr_tok: ["Return","hyperparameters","used","by","your","custom","PyTorch","code","during","model","training","."]
code: def hyperparameters(self):
      """Return hyperparameters used by your custom PyTorch code during model training."""
      hyperparameters = super(PyTorch, self).hyperparameters()
      additional_hyperparameters = (
          self._pytorch_distribution_configuration(
              distribution=self.distribution
          )
      )
      hyperparameters.update(
          EstimatorBase._json_encode_hyperparameters(
              additional_hyperparameters
          )
      )
      if self.compiler_config:
          training_compiler_hyperparameters = (
              self.compiler_config._to_hyperparameter_dict()
          )
          hyperparameters.update(
              EstimatorBase._json_encode_hyperparameters(
                  training_compiler_hyperparameters
              )
          )
  
      return hyperparameters
  
file_path: sagemaker-python-sdk/src/sagemaker/pytorch/estimator.py
imports: from __future__ import absolute_import
  import logging
  from typing import Union, Optional, Dict
  from packaging.version import Version
  from sagemaker.estimator import Framework, EstimatorBase
  from sagemaker.fw_utils import framework_name_from_image, framework_version_from_tag, python_deprecation_warning, validate_version_or_image_args, validate_distribution, profiler_config_deprecation_warning
  from sagemaker.pytorch import defaults
  from sagemaker.pytorch.model import PyTorchModel
  from sagemaker.pytorch.training_compiler.config import TrainingCompilerConfig
  from sagemaker.vpc_utils import VPC_CONFIG_DEFAULT
  from sagemaker.workflow.entities import PipelineVariable
label_desc: Use image node_id 3 for calling the PyTorch obj's underlying member method code with example usage: obj.hyperparameters() and returns: hyperparameters